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Brian Chen

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TMLR Journal 2024 Journal Article

Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning

  • Brian Chen
  • Doruk Aksoy
  • David J Gorsich
  • Shravan Veerapaneni
  • Alex Gorodetsky

We describe a tensor-network latent-space encoding approach for increasing the scalability of behavioral cloning of a video game player’s actions entirely from video streams of the gameplay. Specifically, we address challenges associated with the high computational requirements of traditional deep-learning based encoders such as convolutional variational autoencoders that prohibit their use in widely available hardware or for large scale data. Our approach uses tensor networks instead of deep variational autoencoders for this purpose, and it yields significant speedups with no loss of accuracy. Empirical results on ATARI games demonstrate that our approach leads to a speedup in the time it takes to encode data and train a predictor using the encodings (between 2.6× to 9.6× compared to autoencoders or variational autoencoders). Furthermore, the tensor train encoding can be efficiently trained on CPU as well, which leads to comparable or better training times than the autoencoder and variational autoencoder trained on GPU (0.9× to 5.4× faster). These results suggest significant possibilities in mitigating the need for cost and time-intensive hardware for training deep-learning architectures for behavioral cloning.

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AAAI Conference 2020 Conference Paper

General Partial Label Learning via Dual Bipartite Graph Autoencoder

  • Brian Chen
  • Bo Wu
  • Alireza Zareian
  • Hanwang Zhang
  • Shih-Fu Chang

We formulate a practical yet challenging problem: General Partial Label Learning (GPLL). Compared to the traditional Partial Label Learning (PLL) problem, GPLL relaxes the supervision assumption from instance-level — a label set partially labels an instance — to group-level: 1) a label set partially labels a group of instances, where the within-group instance-label link annotations are missing, and 2) crossgroup links are allowed — instances in a group may be partially linked to the label set from another group. Such ambiguous group-level supervision is more practical in real-world scenarios as additional annotation on the instance-level is no longer required, e. g. , face-naming in videos where the group consists of faces in a frame, labeled by a name set in the corresponding caption. In this paper, we propose a novel graph convolutional network (GCN) called Dual Bipartite Graph Autoencoder (DB-GAE) to tackle the label ambiguity challenge of GPLL. First, we exploit the cross-group correlations to represent the instance groups as dual bipartite graphs: within-group and cross-group, which reciprocally complements each other to resolve the linking ambiguities. Second, we design a GCN autoencoder to encode and decode them, where the decodings are considered as the refined results. It is worth noting that DB-GAE is self-supervised and transductive, as it only uses the group-level supervision without a separate offline training stage. Extensive experiments on two real-world datasets demonstrate that DB-GAE significantly outperforms the best baseline over absolute 0. 159 F1-score and 24. 8% accuracy. We further offer analysis on various levels of label ambiguities.

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